{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以下为利用pandas进行数据读取与保存方法的汇总，请读取sh.600000.csv数据，并进行以下操作"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.利用pandas读取sh.600000.csv格式文件，字段说明如下"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tbody><tr>\n",
    "<td>参数名称\n",
    "</td>\n",
    "<td>参数描述\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>date\n",
    "</td>\n",
    "<td>交易所行情日期\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>code\n",
    "</td>\n",
    "<td>证券代码\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>open\n",
    "</td>\n",
    "<td>开盘价\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>high\n",
    "</td>\n",
    "<td>最高价\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>low\n",
    "</td>\n",
    "<td>最低价\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>close\n",
    "</td>\n",
    "<td>收盘价\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>preclose\n",
    "</td>\n",
    "<td>昨日收盘价\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>volume\n",
    "</td>\n",
    "<td>成交量（累计 单位：股）\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>amount\n",
    "</td>\n",
    "<td>成交额（单位：人民币元）\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>adjustflag\n",
    "</td>\n",
    "<td>复权状态(1：后复权， 2：前复权，3：不复权）\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>turn\n",
    "</td>\n",
    "<td>换手率\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>tradestatus\n",
    "</td>\n",
    "<td>交易状态(1：正常交易 0：停牌）\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>pctChg\n",
    "</td>\n",
    "<td>涨跌幅（百分比）\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>peTTM\n",
    "</td>\n",
    "<td>动态市盈率\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>pbMRQ\n",
    "</td>\n",
    "<td>市净率\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>psTTM\n",
    "</td>\n",
    "<td>市销率\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>pcfNcfTTM\n",
    "</td>\n",
    "<td>市现率\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>isST\n",
    "</td>\n",
    "<td>是否ST股，1是，0否\n",
    "</td></tr></tbody>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"sh.600000.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.查看前5行数据与后5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>peTTM</th>\n",
       "      <th>pbMRQ</th>\n",
       "      <th>psTTM</th>\n",
       "      <th>pcfNcfTTM</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.814685</td>\n",
       "      <td>12.893850</td>\n",
       "      <td>12.775103</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>34802229</td>\n",
       "      <td>451233524.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.123835</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.004418</td>\n",
       "      <td>0.981842</td>\n",
       "      <td>2.310626</td>\n",
       "      <td>-2.043354</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.844372</td>\n",
       "      <td>12.854267</td>\n",
       "      <td>12.735521</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>31296221</td>\n",
       "      <td>404411517.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.111360</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.231479</td>\n",
       "      <td>6.988205</td>\n",
       "      <td>0.979569</td>\n",
       "      <td>2.305277</td>\n",
       "      <td>-2.038624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.784999</td>\n",
       "      <td>12.903745</td>\n",
       "      <td>12.715730</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>36649902</td>\n",
       "      <td>474566888.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.130409</td>\n",
       "      <td>1</td>\n",
       "      <td>0.309358</td>\n",
       "      <td>7.009823</td>\n",
       "      <td>0.982600</td>\n",
       "      <td>2.312409</td>\n",
       "      <td>-2.044930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>30370097</td>\n",
       "      <td>390113096.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.108064</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.696224</td>\n",
       "      <td>6.890921</td>\n",
       "      <td>0.965933</td>\n",
       "      <td>2.273185</td>\n",
       "      <td>-2.010244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-13</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.666253</td>\n",
       "      <td>12.676148</td>\n",
       "      <td>12.498029</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>19345338</td>\n",
       "      <td>246331123.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.068835</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.078433</td>\n",
       "      <td>6.885516</td>\n",
       "      <td>0.965175</td>\n",
       "      <td>2.271402</td>\n",
       "      <td>-2.008667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       code       open       high        low      close  \\\n",
       "0  2017-12-07  sh.600000  12.814685  12.893850  12.775103  12.824581   \n",
       "1  2017-12-08  sh.600000  12.844372  12.854267  12.735521  12.794894   \n",
       "2  2017-12-11  sh.600000  12.784999  12.903745  12.715730  12.834476   \n",
       "3  2017-12-12  sh.600000  12.834476  12.834476  12.606880  12.616775   \n",
       "4  2017-12-13  sh.600000  12.666253  12.676148  12.498029  12.606880   \n",
       "\n",
       "    preclose    volume       amount  adjustflag      turn  tradestatus  \\\n",
       "0  12.824581  34802229  451233524.0           2  0.123835            1   \n",
       "1  12.824581  31296221  404411517.0           2  0.111360            1   \n",
       "2  12.794894  36649902  474566888.0           2  0.130409            1   \n",
       "3  12.834476  30370097  390113096.0           2  0.108064            1   \n",
       "4  12.616775  19345338  246331123.0           2  0.068835            1   \n",
       "\n",
       "     pctChg     peTTM     pbMRQ     psTTM  pcfNcfTTM  isST  \n",
       "0  0.000000  7.004418  0.981842  2.310626  -2.043354     0  \n",
       "1 -0.231479  6.988205  0.979569  2.305277  -2.038624     0  \n",
       "2  0.309358  7.009823  0.982600  2.312409  -2.044930     0  \n",
       "3 -1.696224  6.890921  0.965933  2.273185  -2.010244     0  \n",
       "4 -0.078433  6.885516  0.965175  2.271402  -2.008667     0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>peTTM</th>\n",
       "      <th>pbMRQ</th>\n",
       "      <th>psTTM</th>\n",
       "      <th>pcfNcfTTM</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>11.05</td>\n",
       "      <td>10.84</td>\n",
       "      <td>11.02</td>\n",
       "      <td>10.71</td>\n",
       "      <td>43049070</td>\n",
       "      <td>472438382.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.153179</td>\n",
       "      <td>1</td>\n",
       "      <td>2.894495</td>\n",
       "      <td>5.820451</td>\n",
       "      <td>0.760281</td>\n",
       "      <td>1.899833</td>\n",
       "      <td>3.113035</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.08</td>\n",
       "      <td>10.97</td>\n",
       "      <td>11.08</td>\n",
       "      <td>11.02</td>\n",
       "      <td>20811293</td>\n",
       "      <td>229713745.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.074052</td>\n",
       "      <td>1</td>\n",
       "      <td>0.544460</td>\n",
       "      <td>5.852141</td>\n",
       "      <td>0.764420</td>\n",
       "      <td>1.910177</td>\n",
       "      <td>3.129985</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>11.10</td>\n",
       "      <td>10.96</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.08</td>\n",
       "      <td>25491217</td>\n",
       "      <td>281189602.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.090704</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.722021</td>\n",
       "      <td>5.809888</td>\n",
       "      <td>0.758901</td>\n",
       "      <td>1.896385</td>\n",
       "      <td>3.107385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.90</td>\n",
       "      <td>11.00</td>\n",
       "      <td>23597997</td>\n",
       "      <td>256533148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083967</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.909094</td>\n",
       "      <td>5.757070</td>\n",
       "      <td>0.752002</td>\n",
       "      <td>1.879146</td>\n",
       "      <td>3.079136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>10.94</td>\n",
       "      <td>11.03</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10580550</td>\n",
       "      <td>115760323.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.037648</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.091736</td>\n",
       "      <td>5.751789</td>\n",
       "      <td>0.751312</td>\n",
       "      <td>1.877422</td>\n",
       "      <td>3.076312</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date       code   open   high    low  close  preclose    volume  \\\n",
       "240  2018-12-03  sh.600000  10.99  11.05  10.84  11.02     10.71  43049070   \n",
       "241  2018-12-04  sh.600000  11.00  11.08  10.97  11.08     11.02  20811293   \n",
       "242  2018-12-05  sh.600000  10.99  11.10  10.96  11.00     11.08  25491217   \n",
       "243  2018-12-06  sh.600000  10.82  10.93  10.82  10.90     11.00  23597997   \n",
       "244  2018-12-07  sh.600000  10.94  11.03  10.88  10.89     10.90  10580550   \n",
       "\n",
       "          amount  adjustflag      turn  tradestatus    pctChg     peTTM  \\\n",
       "240  472438382.0           2  0.153179            1  2.894495  5.820451   \n",
       "241  229713745.0           2  0.074052            1  0.544460  5.852141   \n",
       "242  281189602.0           2  0.090704            1 -0.722021  5.809888   \n",
       "243  256533148.0           2  0.083967            1 -0.909094  5.757070   \n",
       "244  115760323.0           2  0.037648            1 -0.091736  5.751789   \n",
       "\n",
       "        pbMRQ     psTTM  pcfNcfTTM  isST  \n",
       "240  0.760281  1.899833   3.113035     0  \n",
       "241  0.764420  1.910177   3.129985     0  \n",
       "242  0.758901  1.896385   3.107385     0  \n",
       "243  0.752002  1.879146   3.079136     0  \n",
       "244  0.751312  1.877422   3.076312     0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.查看该份数据的描述统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>peTTM</th>\n",
       "      <th>pbMRQ</th>\n",
       "      <th>psTTM</th>\n",
       "      <th>pcfNcfTTM</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>2.450000e+02</td>\n",
       "      <td>2.450000e+02</td>\n",
       "      <td>245.0</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.0</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.095712</td>\n",
       "      <td>11.189029</td>\n",
       "      <td>10.992367</td>\n",
       "      <td>11.084785</td>\n",
       "      <td>11.092682</td>\n",
       "      <td>3.582759e+07</td>\n",
       "      <td>4.270868e+08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.127483</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.083437</td>\n",
       "      <td>6.019194</td>\n",
       "      <td>0.822677</td>\n",
       "      <td>1.960009</td>\n",
       "      <td>8.323278</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.132764</td>\n",
       "      <td>1.144871</td>\n",
       "      <td>1.125110</td>\n",
       "      <td>1.136245</td>\n",
       "      <td>1.141595</td>\n",
       "      <td>4.524912e+07</td>\n",
       "      <td>6.108954e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.161007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.392350</td>\n",
       "      <td>0.643421</td>\n",
       "      <td>0.104405</td>\n",
       "      <td>0.200529</td>\n",
       "      <td>10.900488</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.163242</td>\n",
       "      <td>9.252302</td>\n",
       "      <td>9.074183</td>\n",
       "      <td>9.163242</td>\n",
       "      <td>9.163242</td>\n",
       "      <td>8.995716e+06</td>\n",
       "      <td>9.169444e+07</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.032009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.391040</td>\n",
       "      <td>5.023942</td>\n",
       "      <td>0.682401</td>\n",
       "      <td>1.644255</td>\n",
       "      <td>-2.152143</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.170000</td>\n",
       "      <td>10.270000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>10.170000</td>\n",
       "      <td>10.170000</td>\n",
       "      <td>1.755753e+07</td>\n",
       "      <td>1.882069e+08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.062474</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.771871</td>\n",
       "      <td>5.495953</td>\n",
       "      <td>0.735460</td>\n",
       "      <td>1.798737</td>\n",
       "      <td>-1.958214</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.815792</td>\n",
       "      <td>10.885061</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>10.786106</td>\n",
       "      <td>10.786106</td>\n",
       "      <td>2.294848e+07</td>\n",
       "      <td>2.417497e+08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.081656</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.810628</td>\n",
       "      <td>0.774275</td>\n",
       "      <td>1.899833</td>\n",
       "      <td>10.895982</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.280328</td>\n",
       "      <td>12.349596</td>\n",
       "      <td>12.230850</td>\n",
       "      <td>12.290223</td>\n",
       "      <td>12.329805</td>\n",
       "      <td>3.148443e+07</td>\n",
       "      <td>3.724712e+08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.112029</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.631583</td>\n",
       "      <td>6.721107</td>\n",
       "      <td>0.940932</td>\n",
       "      <td>2.161991</td>\n",
       "      <td>12.496256</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.507371</td>\n",
       "      <td>13.853714</td>\n",
       "      <td>13.220401</td>\n",
       "      <td>13.507371</td>\n",
       "      <td>13.507371</td>\n",
       "      <td>3.796536e+08</td>\n",
       "      <td>5.099364e+09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.350900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.193799</td>\n",
       "      <td>7.386724</td>\n",
       "      <td>1.034116</td>\n",
       "      <td>2.376102</td>\n",
       "      <td>31.326051</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open        high         low       close    preclose  \\\n",
       "count  245.000000  245.000000  245.000000  245.000000  245.000000   \n",
       "mean    11.095712   11.189029   10.992367   11.084785   11.092682   \n",
       "std      1.132764    1.144871    1.125110    1.136245    1.141595   \n",
       "min      9.163242    9.252302    9.074183    9.163242    9.163242   \n",
       "25%     10.170000   10.270000   10.100000   10.170000   10.170000   \n",
       "50%     10.815792   10.885061   10.700000   10.786106   10.786106   \n",
       "75%     12.280328   12.349596   12.230850   12.290223   12.329805   \n",
       "max     13.507371   13.853714   13.220401   13.507371   13.507371   \n",
       "\n",
       "             volume        amount  adjustflag        turn  tradestatus  \\\n",
       "count  2.450000e+02  2.450000e+02       245.0  245.000000        245.0   \n",
       "mean   3.582759e+07  4.270868e+08         2.0    0.127483          1.0   \n",
       "std    4.524912e+07  6.108954e+08         0.0    0.161007          0.0   \n",
       "min    8.995716e+06  9.169444e+07         2.0    0.032009          1.0   \n",
       "25%    1.755753e+07  1.882069e+08         2.0    0.062474          1.0   \n",
       "50%    2.294848e+07  2.417497e+08         2.0    0.081656          1.0   \n",
       "75%    3.148443e+07  3.724712e+08         2.0    0.112029          1.0   \n",
       "max    3.796536e+08  5.099364e+09         2.0    1.350900          1.0   \n",
       "\n",
       "           pctChg       peTTM       pbMRQ       psTTM   pcfNcfTTM   isST  \n",
       "count  245.000000  245.000000  245.000000  245.000000  245.000000  245.0  \n",
       "mean    -0.083437    6.019194    0.822677    1.960009    8.323278    0.0  \n",
       "std      1.392350    0.643421    0.104405    0.200529   10.900488    0.0  \n",
       "min     -5.391040    5.023942    0.682401    1.644255   -2.152143    0.0  \n",
       "25%     -0.771871    5.495953    0.735460    1.798737   -1.958214    0.0  \n",
       "50%      0.000000    5.810628    0.774275    1.899833   10.895982    0.0  \n",
       "75%      0.631583    6.721107    0.940932    2.161991   12.496256    0.0  \n",
       "max      5.193799    7.386724    1.034116    2.376102   31.326051    0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.查看该份数据的字段概况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 245 entries, 0 to 244\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   date         245 non-null    object \n",
      " 1   code         245 non-null    object \n",
      " 2   open         245 non-null    float64\n",
      " 3   high         245 non-null    float64\n",
      " 4   low          245 non-null    float64\n",
      " 5   close        245 non-null    float64\n",
      " 6   preclose     245 non-null    float64\n",
      " 7   volume       245 non-null    int64  \n",
      " 8   amount       245 non-null    float64\n",
      " 9   adjustflag   245 non-null    int64  \n",
      " 10  turn         245 non-null    float64\n",
      " 11  tradestatus  245 non-null    int64  \n",
      " 12  pctChg       245 non-null    float64\n",
      " 13  peTTM        245 non-null    float64\n",
      " 14  pbMRQ        245 non-null    float64\n",
      " 15  psTTM        245 non-null    float64\n",
      " 16  pcfNcfTTM    245 non-null    float64\n",
      " 17  isST         245 non-null    int64  \n",
      "dtypes: float64(12), int64(4), object(2)\n",
      "memory usage: 34.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.数据的选取（索引方式）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "对DataFrame进行选择，主要有以下几种索引方式\n",
    "\n",
    "1)\t使用[ ]\n",
    "\n",
    "2)\tdf.loc[ ]\n",
    "\n",
    "3)\tdf.iloc[ ]（此种方式为标签位置索引，感兴趣的可自行了解）\n",
    "\n",
    "4)\tdf.ix[ ]（此种方式逐渐被淘汰，此处不做讲解）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (1)使用 [ ]位置索引方式，选取出前3行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>peTTM</th>\n",
       "      <th>pbMRQ</th>\n",
       "      <th>psTTM</th>\n",
       "      <th>pcfNcfTTM</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.814685</td>\n",
       "      <td>12.893850</td>\n",
       "      <td>12.775103</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>34802229</td>\n",
       "      <td>451233524.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.123835</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.004418</td>\n",
       "      <td>0.981842</td>\n",
       "      <td>2.310626</td>\n",
       "      <td>-2.043354</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.844372</td>\n",
       "      <td>12.854267</td>\n",
       "      <td>12.735521</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>31296221</td>\n",
       "      <td>404411517.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.111360</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.231479</td>\n",
       "      <td>6.988205</td>\n",
       "      <td>0.979569</td>\n",
       "      <td>2.305277</td>\n",
       "      <td>-2.038624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.784999</td>\n",
       "      <td>12.903745</td>\n",
       "      <td>12.715730</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>36649902</td>\n",
       "      <td>474566888.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.130409</td>\n",
       "      <td>1</td>\n",
       "      <td>0.309358</td>\n",
       "      <td>7.009823</td>\n",
       "      <td>0.982600</td>\n",
       "      <td>2.312409</td>\n",
       "      <td>-2.044930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       code       open       high        low      close  \\\n",
       "0  2017-12-07  sh.600000  12.814685  12.893850  12.775103  12.824581   \n",
       "1  2017-12-08  sh.600000  12.844372  12.854267  12.735521  12.794894   \n",
       "2  2017-12-11  sh.600000  12.784999  12.903745  12.715730  12.834476   \n",
       "\n",
       "    preclose    volume       amount  adjustflag      turn  tradestatus  \\\n",
       "0  12.824581  34802229  451233524.0           2  0.123835            1   \n",
       "1  12.824581  31296221  404411517.0           2  0.111360            1   \n",
       "2  12.794894  36649902  474566888.0           2  0.130409            1   \n",
       "\n",
       "     pctChg     peTTM     pbMRQ     psTTM  pcfNcfTTM  isST  \n",
       "0  0.000000  7.004418  0.981842  2.310626  -2.043354     0  \n",
       "1 -0.231479  6.988205  0.979569  2.305277  -2.038624     0  \n",
       "2  0.309358  7.009823  0.982600  2.312409  -2.044930     0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (2)使用[ ]标签索引方式，选取出“date”一列，并用type查看返回的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2017-12-07\n",
       "1    2017-12-08\n",
       "2    2017-12-11\n",
       "3    2017-12-12\n",
       "4    2017-12-13\n",
       "5    2017-12-14\n",
       "6    2017-12-15\n",
       "7    2017-12-18\n",
       "8    2017-12-19\n",
       "9    2017-12-20\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'].head(10) #选取一列，返回serises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['close'].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ##### (3)使用[ ]标签索引方式，取出“date”、“open”列和“colse”列，并用type查看返回的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>12.814685</td>\n",
       "      <td>12.824581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>12.844372</td>\n",
       "      <td>12.794894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>12.784999</td>\n",
       "      <td>12.834476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.616775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-13</td>\n",
       "      <td>12.666253</td>\n",
       "      <td>12.606880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open      close\n",
       "0  2017-12-07  12.814685  12.824581\n",
       "1  2017-12-08  12.844372  12.794894\n",
       "2  2017-12-11  12.784999  12.834476\n",
       "3  2017-12-12  12.834476  12.616775\n",
       "4  2017-12-13  12.666253  12.606880"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['date','open','close']].head(5)   #注意这里的中括号的数量，选取多列,通过list传递，返回DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[['date','open','close']].head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (4)使用[ ]标签索引方式，选取收盘价大于11.08的所有数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>peTTM</th>\n",
       "      <th>pbMRQ</th>\n",
       "      <th>psTTM</th>\n",
       "      <th>pcfNcfTTM</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.814685</td>\n",
       "      <td>12.893850</td>\n",
       "      <td>12.775103</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>34802229</td>\n",
       "      <td>451233524.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.123835</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.004418</td>\n",
       "      <td>0.981842</td>\n",
       "      <td>2.310626</td>\n",
       "      <td>-2.043354</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-08</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.844372</td>\n",
       "      <td>12.854267</td>\n",
       "      <td>12.735521</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>31296221</td>\n",
       "      <td>404411517.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.111360</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.231479</td>\n",
       "      <td>6.988205</td>\n",
       "      <td>0.979569</td>\n",
       "      <td>2.305277</td>\n",
       "      <td>-2.038624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-11</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.784999</td>\n",
       "      <td>12.903745</td>\n",
       "      <td>12.715730</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>36649902</td>\n",
       "      <td>474566888.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.130409</td>\n",
       "      <td>1</td>\n",
       "      <td>0.309358</td>\n",
       "      <td>7.009823</td>\n",
       "      <td>0.982600</td>\n",
       "      <td>2.312409</td>\n",
       "      <td>-2.044930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-12</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>30370097</td>\n",
       "      <td>390113096.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.108064</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.696224</td>\n",
       "      <td>6.890921</td>\n",
       "      <td>0.965933</td>\n",
       "      <td>2.273185</td>\n",
       "      <td>-2.010244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-13</td>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.666253</td>\n",
       "      <td>12.676148</td>\n",
       "      <td>12.498029</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>19345338</td>\n",
       "      <td>246331123.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.068835</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.078433</td>\n",
       "      <td>6.885516</td>\n",
       "      <td>0.965175</td>\n",
       "      <td>2.271402</td>\n",
       "      <td>-2.008667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       code       open       high        low      close  \\\n",
       "0  2017-12-07  sh.600000  12.814685  12.893850  12.775103  12.824581   \n",
       "1  2017-12-08  sh.600000  12.844372  12.854267  12.735521  12.794894   \n",
       "2  2017-12-11  sh.600000  12.784999  12.903745  12.715730  12.834476   \n",
       "3  2017-12-12  sh.600000  12.834476  12.834476  12.606880  12.616775   \n",
       "4  2017-12-13  sh.600000  12.666253  12.676148  12.498029  12.606880   \n",
       "\n",
       "    preclose    volume       amount  adjustflag      turn  tradestatus  \\\n",
       "0  12.824581  34802229  451233524.0           2  0.123835            1   \n",
       "1  12.824581  31296221  404411517.0           2  0.111360            1   \n",
       "2  12.794894  36649902  474566888.0           2  0.130409            1   \n",
       "3  12.834476  30370097  390113096.0           2  0.108064            1   \n",
       "4  12.616775  19345338  246331123.0           2  0.068835            1   \n",
       "\n",
       "     pctChg     peTTM     pbMRQ     psTTM  pcfNcfTTM  isST  \n",
       "0  0.000000  7.004418  0.981842  2.310626  -2.043354     0  \n",
       "1 -0.231479  6.988205  0.979569  2.305277  -2.038624     0  \n",
       "2  0.309358  7.009823  0.982600  2.312409  -2.044930     0  \n",
       "3 -1.696224  6.890921  0.965933  2.273185  -2.010244     0  \n",
       "4 -0.078433  6.885516  0.965175  2.271402  -2.008667     0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['open']>11.08].head() #<布尔数组>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （5）将date设置为索引，并重新命名数据库为df1，使用df.loc[]标签定位方式，选取df1数据库中2017-12-11至2017-12-20的所有数据\n",
    "\n",
    "df.loc[行标签,列标签]\n",
    "\n",
    "df.loc['a':'b'] #选取 ab 两行数据\n",
    "\n",
    "df.loc[:,'open'] #选取 open 列的数据\n",
    "\n",
    "df.loc 的第一个参数是行标签，第二个参数为列标签（可选参数，默认为所有列标签）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-07</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.814685</td>\n",
       "      <td>12.893850</td>\n",
       "      <td>12.775103</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>34802229</td>\n",
       "      <td>451233524.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.123835</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.004418</td>\n",
       "      <td>0.981842</td>\n",
       "      <td>2.310626</td>\n",
       "      <td>-2.043354</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.844372</td>\n",
       "      <td>12.854267</td>\n",
       "      <td>12.735521</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>12.824581</td>\n",
       "      <td>31296221</td>\n",
       "      <td>404411517.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.111360</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.231479</td>\n",
       "      <td>6.988205</td>\n",
       "      <td>0.979569</td>\n",
       "      <td>2.305277</td>\n",
       "      <td>-2.038624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.784999</td>\n",
       "      <td>12.903745</td>\n",
       "      <td>12.715730</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.794894</td>\n",
       "      <td>36649902</td>\n",
       "      <td>474566888.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.130409</td>\n",
       "      <td>1</td>\n",
       "      <td>0.309358</td>\n",
       "      <td>7.009823</td>\n",
       "      <td>0.982600</td>\n",
       "      <td>2.312409</td>\n",
       "      <td>-2.044930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>30370097</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-2.010244</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.666253</td>\n",
       "      <td>12.676148</td>\n",
       "      <td>12.498029</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
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       "      <td>246331123.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>-0.078433</td>\n",
       "      <td>6.885516</td>\n",
       "      <td>0.965175</td>\n",
       "      <td>2.271402</td>\n",
       "      <td>-2.008667</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "                 code       open       high        low      close   preclose  \\\n",
       "date                                                                           \n",
       "2017-12-07  sh.600000  12.814685  12.893850  12.775103  12.824581  12.824581   \n",
       "2017-12-08  sh.600000  12.844372  12.854267  12.735521  12.794894  12.824581   \n",
       "2017-12-11  sh.600000  12.784999  12.903745  12.715730  12.834476  12.794894   \n",
       "2017-12-12  sh.600000  12.834476  12.834476  12.606880  12.616775  12.834476   \n",
       "2017-12-13  sh.600000  12.666253  12.676148  12.498029  12.606880  12.616775   \n",
       "\n",
       "              volume       amount  adjustflag      turn  tradestatus  \\\n",
       "date                                                                   \n",
       "2017-12-07  34802229  451233524.0           2  0.123835            1   \n",
       "2017-12-08  31296221  404411517.0           2  0.111360            1   \n",
       "2017-12-11  36649902  474566888.0           2  0.130409            1   \n",
       "2017-12-12  30370097  390113096.0           2  0.108064            1   \n",
       "2017-12-13  19345338  246331123.0           2  0.068835            1   \n",
       "\n",
       "              pctChg     peTTM     pbMRQ     psTTM  pcfNcfTTM  isST  \n",
       "date                                                                 \n",
       "2017-12-07  0.000000  7.004418  0.981842  2.310626  -2.043354     0  \n",
       "2017-12-08 -0.231479  6.988205  0.979569  2.305277  -2.038624     0  \n",
       "2017-12-11  0.309358  7.009823  0.982600  2.312409  -2.044930     0  \n",
       "2017-12-12 -1.696224  6.890921  0.965933  2.273185  -2.010244     0  \n",
       "2017-12-13 -0.078433  6.885516  0.965175  2.271402  -2.008667     0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.set_index(['date'])#指定索引\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2017-12-11</th>\n",
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       "      <td>12.903745</td>\n",
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       "      <td>-2.044930</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.606880</td>\n",
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       "      <td>-2.010244</td>\n",
       "      <td>0</td>\n",
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       "      <th>2017-12-13</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.666253</td>\n",
       "      <td>12.676148</td>\n",
       "      <td>12.498029</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>19345338</td>\n",
       "      <td>246331123.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.068835</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.078433</td>\n",
       "      <td>6.885516</td>\n",
       "      <td>0.965175</td>\n",
       "      <td>2.271402</td>\n",
       "      <td>-2.008667</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.656357</td>\n",
       "      <td>12.656357</td>\n",
       "      <td>12.527716</td>\n",
       "      <td>12.557402</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>16141678</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.057436</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.392466</td>\n",
       "      <td>6.858493</td>\n",
       "      <td>0.961387</td>\n",
       "      <td>2.262488</td>\n",
       "      <td>-2.000784</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.557402</td>\n",
       "      <td>12.596984</td>\n",
       "      <td>12.478238</td>\n",
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       "      <td>12.557402</td>\n",
       "      <td>16210108</td>\n",
       "      <td>205323767.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.057679</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.551613</td>\n",
       "      <td>6.820661</td>\n",
       "      <td>0.956084</td>\n",
       "      <td>2.250008</td>\n",
       "      <td>-1.989747</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.577193</td>\n",
       "      <td>12.577193</td>\n",
       "      <td>12.478238</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.237716</td>\n",
       "      <td>6.836874</td>\n",
       "      <td>0.958357</td>\n",
       "      <td>2.255356</td>\n",
       "      <td>-1.994477</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.517820</td>\n",
       "      <td>12.626671</td>\n",
       "      <td>12.488134</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>12.517820</td>\n",
       "      <td>18399603</td>\n",
       "      <td>233884666.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.065470</td>\n",
       "      <td>1</td>\n",
       "      <td>0.790517</td>\n",
       "      <td>6.890921</td>\n",
       "      <td>0.965933</td>\n",
       "      <td>2.273185</td>\n",
       "      <td>-2.010244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20</th>\n",
       "      <td>sh.600000</td>\n",
       "      <td>12.626671</td>\n",
       "      <td>12.626671</td>\n",
       "      <td>12.468343</td>\n",
       "      <td>12.596984</td>\n",
       "      <td>12.616775</td>\n",
       "      <td>26639844</td>\n",
       "      <td>337603451.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.094791</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.156866</td>\n",
       "      <td>6.880112</td>\n",
       "      <td>0.964417</td>\n",
       "      <td>2.269619</td>\n",
       "      <td>-2.007090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 code       open       high        low      close   preclose  \\\n",
       "date                                                                           \n",
       "2017-12-11  sh.600000  12.784999  12.903745  12.715730  12.834476  12.794894   \n",
       "2017-12-12  sh.600000  12.834476  12.834476  12.606880  12.616775  12.834476   \n",
       "2017-12-13  sh.600000  12.666253  12.676148  12.498029  12.606880  12.616775   \n",
       "2017-12-14  sh.600000  12.656357  12.656357  12.527716  12.557402  12.606880   \n",
       "2017-12-15  sh.600000  12.557402  12.596984  12.478238  12.488134  12.557402   \n",
       "2017-12-18  sh.600000  12.577193  12.577193  12.478238  12.517820  12.488134   \n",
       "2017-12-19  sh.600000  12.517820  12.626671  12.488134  12.616775  12.517820   \n",
       "2017-12-20  sh.600000  12.626671  12.626671  12.468343  12.596984  12.616775   \n",
       "\n",
       "              volume       amount  adjustflag      turn  tradestatus  \\\n",
       "date                                                                   \n",
       "2017-12-11  36649902  474566888.0           2  0.130409            1   \n",
       "2017-12-12  30370097  390113096.0           2  0.108064            1   \n",
       "2017-12-13  19345338  246331123.0           2  0.068835            1   \n",
       "2017-12-14  16141678  205219487.0           2  0.057436            1   \n",
       "2017-12-15  16210108  205323767.0           2  0.057679            1   \n",
       "2017-12-18  13445648  170140035.0           2  0.047843            1   \n",
       "2017-12-19  18399603  233884666.0           2  0.065470            1   \n",
       "2017-12-20  26639844  337603451.0           2  0.094791            1   \n",
       "\n",
       "              pctChg     peTTM     pbMRQ     psTTM  pcfNcfTTM  isST  \n",
       "date                                                                 \n",
       "2017-12-11  0.309358  7.009823  0.982600  2.312409  -2.044930     0  \n",
       "2017-12-12 -1.696224  6.890921  0.965933  2.273185  -2.010244     0  \n",
       "2017-12-13 -0.078433  6.885516  0.965175  2.271402  -2.008667     0  \n",
       "2017-12-14 -0.392466  6.858493  0.961387  2.262488  -2.000784     0  \n",
       "2017-12-15 -0.551613  6.820661  0.956084  2.250008  -1.989747     0  \n",
       "2017-12-18  0.237716  6.836874  0.958357  2.255356  -1.994477     0  \n",
       "2017-12-19  0.790517  6.890921  0.965933  2.273185  -2.010244     0  \n",
       "2017-12-20 -0.156866  6.880112  0.964417  2.269619  -2.007090     0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['2017-12-11':'2017-12-20']#行索引指定"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （6）使用df.loc[]标签定位方式，选取df1数据库中open至close间的所有列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-07</th>\n",
       "      <td>12.814685</td>\n",
       "      <td>12.893850</td>\n",
       "      <td>12.775103</td>\n",
       "      <td>12.824581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-08</th>\n",
       "      <td>12.844372</td>\n",
       "      <td>12.854267</td>\n",
       "      <td>12.735521</td>\n",
       "      <td>12.794894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-11</th>\n",
       "      <td>12.784999</td>\n",
       "      <td>12.903745</td>\n",
       "      <td>12.715730</td>\n",
       "      <td>12.834476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-12</th>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.834476</td>\n",
       "      <td>12.606880</td>\n",
       "      <td>12.616775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>12.666253</td>\n",
       "      <td>12.676148</td>\n",
       "      <td>12.498029</td>\n",
       "      <td>12.606880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close\n",
       "date                                                  \n",
       "2017-12-07  12.814685  12.893850  12.775103  12.824581\n",
       "2017-12-08  12.844372  12.854267  12.735521  12.794894\n",
       "2017-12-11  12.784999  12.903745  12.715730  12.834476\n",
       "2017-12-12  12.834476  12.834476  12.606880  12.616775\n",
       "2017-12-13  12.666253  12.676148  12.498029  12.606880"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[:,'open':'close'].head() #列索引指定"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （7）使用df.loc[]标签定位方式，选取df1数据库中时间从2018-07-01至2018-07-08，并open至close间的所有列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>9.450212</td>\n",
       "      <td>9.450212</td>\n",
       "      <td>9.133556</td>\n",
       "      <td>9.192929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>9.192929</td>\n",
       "      <td>9.281988</td>\n",
       "      <td>9.103869</td>\n",
       "      <td>9.252302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>9.242406</td>\n",
       "      <td>9.321570</td>\n",
       "      <td>9.183033</td>\n",
       "      <td>9.212720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>9.163242</td>\n",
       "      <td>9.252302</td>\n",
       "      <td>9.123660</td>\n",
       "      <td>9.163242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-06</th>\n",
       "      <td>9.212720</td>\n",
       "      <td>9.331466</td>\n",
       "      <td>9.074183</td>\n",
       "      <td>9.272093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high       low     close\n",
       "date                                              \n",
       "2018-07-02  9.450212  9.450212  9.133556  9.192929\n",
       "2018-07-03  9.192929  9.281988  9.103869  9.252302\n",
       "2018-07-04  9.242406  9.321570  9.183033  9.212720\n",
       "2018-07-05  9.163242  9.252302  9.123660  9.163242\n",
       "2018-07-06  9.212720  9.331466  9.074183  9.272093"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['2018-07-01':'2018-07-08','open':'close'].head()# 行列全部指定"
   ]
  }
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